PERBANDINGAN METODE EXTREME LEARNING MACHINE DAN BACKPROPAGATION UNTUK MENGKLASIFIKASI PHISING WEBSITES
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Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006)., Extreme Learning Machine : Theory and Applications. Extreme Learning Machine : Theory and Applications, 490-501 Goel, A., & Sharma, D. (2014). Prevention from hacking attacks: Phishing Detection Using Associative Classification Data Mining. Lesnussa, Y. A., Sinay, L. J., & Idah, M. R. (2017). Aplikasi Jaringan Saraf Tiruan Backpropagation untuk Penyebaran Penyakit Demam Berdarah Dengue (DBD) di Kota Ambon. Jurnal Matematika Integratif, 13(2), 63-72. APWG Q1 Report (2019). Phishing Activity Trends Report 1st Quarter 2019. Jeeva, S. C., & Rajsingh, E. B. (2016). Intelligent phishing url detection using association rule mining. Human-centric Computing and Information Sciences, 6(1), 10. Jeeva, S. C., & Rajsingh, E. B. (2016). Intelligent phishing url detection using association rule mining. Human-centric Computing and Information Sciences, 6(1), 10. Tan, P. N. (2018). Introduction to data mining. Pearson Education India. Vercellis, C. (2009). Business intelligence: data mining and optimization for decision making. New York: Wiley. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. Dadkhah, M., & Sutikno, T. (2015). Phishing or hijacking? Forgers hijacked DU journal by copying content of another authenticate journal. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 3(3), 119-120. Sharma, A., Singh, P., & Kaur, A. (2015). Phishing Websites Detection Using Back Propagation Algorithm: A Review. Abdelhamid, N., Ayesh, A., & Thabtah, F. (2014). Phishing detection based associative classification data mining. Expert Systems with Applications, 41(13), 5948-5959. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. Rachmawati, D. (2014). Phising Sebagai Salah Satu Bentuk Ancaman Dalam Dunia Cyber. Jurnal SAINTIKOM Vol, 13(3), 210. Hansi, J., Dongsong, Z., & Zhijun, Y. (2013). A Classification Model for Detection of Chinese Phishing e-Business Websites. PACIS Proceedings. Widodo, S. (2017). Klasifikasi Situs Phishing dengan Menggunakan Neural Network dan K-Nearest Neighbor. Information Management for Educators and Professionals, 1(2), 145-154. Ali, W. (2017). Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection. International Journal of Advanced Computer Science and Applications, 8(9), 72-78. Zareapoor, M., & Seeja, K. R. (2015). Feature extraction or feature selection for text classification: A case study on phishing email detection. International Journal of Information Engineering and Electronic Business, 7(2), 60. Gurney, K. (2014). An introduction to neural networks. CRC press.
//remove to enable link on refferencesHuang, G. B., Zhu, Q. Y., & Siew, C. K. (2006)., Extreme Learning Machine : Theory and Applications. Extreme Learning Machine : Theory and Applications, 490-501 Goel, A., & Sharma, D. (2014). Prevention from hacking attacks: Phishing Detection Using Associative Classification Data Mining. Lesnussa, Y. A., Sinay, L. J., & Idah, M. R. (2017). Aplikasi Jaringan Saraf Tiruan Backpropagation untuk Penyebaran Penyakit Demam Berdarah Dengue (DBD) di Kota Ambon. Jurnal Matematika Integratif, 13(2), 63-72. APWG Q1 Report (2019). Phishing Activity Trends Report 1st Quarter 2019. Jeeva, S. C., & Rajsingh, E. B. (2016). Intelligent phishing url detection using association rule mining. Human-centric Computing and Information Sciences, 6(1), 10. Jeeva, S. C., & Rajsingh, E. B. (2016). Intelligent phishing url detection using association rule mining. Human-centric Computing and Information Sciences, 6(1), 10. Tan, P. N. (2018). Introduction to data mining. Pearson Education India. Vercellis, C. (2009). Business intelligence: data mining and optimization for decision making. New York: Wiley. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. Dadkhah, M., & Sutikno, T. (2015). Phishing or hijacking? Forgers hijacked DU journal by copying content of another authenticate journal. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 3(3), 119-120. Sharma, A., Singh, P., & Kaur, A. (2015). Phishing Websites Detection Using Back Propagation Algorithm: A Review. Abdelhamid, N., Ayesh, A., & Thabtah, F. (2014). Phishing detection based associative classification data mining. Expert Systems with Applications, 41(13), 5948-5959. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. Rachmawati, D. (2014). Phising Sebagai Salah Satu Bentuk Ancaman Dalam Dunia Cyber. Jurnal SAINTIKOM Vol, 13(3), 210. Hansi, J., Dongsong, Z., & Zhijun, Y. (2013). A Classification Model for Detection of Chinese Phishing e-Business Websites. PACIS Proceedings. Widodo, S. (2017). Klasifikasi Situs Phishing dengan Menggunakan Neural Network dan K-Nearest Neighbor. Information Management for Educators and Professionals, 1(2), 145-154. Ali, W. (2017). Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection. International Journal of Advanced Computer Science and Applications, 8(9), 72-78. Zareapoor, M., & Seeja, K. R. (2015). Feature extraction or feature selection for text classification: A case study on phishing email detection. International Journal of Information Engineering and Electronic Business, 7(2), 60. Gurney, K. (2014). An introduction to neural networks. CRC press.
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